README.md

AgentTool Resilience: Timeout, Retry, and Redirect Patterns

This sample demonstrates how to handle failures, timeouts, and partial results from downstream agents in multi-agent workflows using ADK.

Running the Demo

adk web contributing/samples/agent_tool_resilience

Then in the web UI, select agent_tool_resilience from the dropdown and try:

1. Simple query: "What is quantum computing?"
2. Complex query: (very detailed research request) as "Research quantum computing applications in healthcare, finance, cryptography, logistics, weather prediction, drug discovery, and machine learning. For each domain, provide: historical context, current state-of-the-art, technical challenges, recent breakthroughs in 2024, comparison with classical approaches, economic impact, and future roadmap for the next 10 years."
3. Timeout scenario: Set timeout to 5 seconds in agent.py and use a complex query

Features Demonstrated

• Timeout Protection: Custom TimeoutAgentTool wrapper adds timeout handling to sub-agents
• Automatic Retry: ReflectAndRetryToolPlugin handles retries with structured guidance
• Dynamic Fallback: Coordinator agent routes to alternative agents when primary fails
• Error Recovery: Specialized agent provides user-friendly error analysis

Expected Behavior

1. Normal Operation: Primary agent handles the query successfully
2. Timeout Scenario: Primary times out → Fallback agent is automatically tried
3. Failure Scenario: Primary fails → Retry → Fallback → Error recovery agent provides guidance

Architecture

The sample includes:

• coordinator_agent - Routes requests and handles errors
• research_agent_primary - Primary agent with timeout protection (5s)
• research_agent_fallback - Fallback agent with longer timeout (60s)
• error_recovery_agent - Analyzes failures and provides recommendations